Mobile robots have become indispensable for exploring hostile environments, such as in space or disaster relief scenarios, but often remain limited to teleoperation by a human operator. This restricts the deployment scale and requires near-continuous low-latency communication between the operator and the robot. We present MOSAIC: a scalable autonomy framework for multi-robot scientific exploration using a unified mission abstraction based on Points of Interest (POIs) and multiple layers of autonomy, enabling supervision by a single operator. The framework dynamically allocates exploration and measurement tasks based on each robot's capabilities, leveraging team-level redundancy and specialization to enable continuous operation. We validated the framework in a space-analog field experiment emulating a lunar prospecting scenario, involving a heterogeneous team of five robots and a single operator. Despite the complete failure of one robot during the mission, the team completed 82.3% of assigned tasks at an Autonomy Ratio of 86%, while the operator workload remained at only 78.2%. These results demonstrate that the proposed framework enables robust, scalable multi-robot scientific exploration with limited operator intervention. We further derive practical lessons learned in robot interoperability, networking architecture, team composition, and operator workload management to inform future multi-robot exploration missions.

翻译：移动机器人已成为探索太空或救灾等恶劣环境不可或缺的工具，但通常仍局限于由人类操作员进行遥操作。这限制了部署规模，并要求操作员与机器人之间保持近乎连续的低延迟通信。本文提出MOSAIC：一种基于兴趣点统一任务抽象与多层自主性的可扩展多机器人科学探索自主框架，支持单操作员监督。该框架根据各机器人能力动态分配探索与测量任务，利用团队级冗余与专业化实现持续作业。我们通过在模拟月球勘探场景的空间类比实地实验中验证了该框架，实验涉及包含五个机器人的异构团队及单名操作员。尽管任务期间有一台机器人完全失效，团队仍以86%的自主率完成了82.3%的分配任务，且操作员工作负荷仅维持在78.2%。结果表明，所提框架能够在有限人工干预下实现鲁棒、可扩展的多机器人科学探索。我们进一步总结了在机器人互操作性、网络架构、团队构成及操作员负荷管理方面的实践经验，为未来多机器人探索任务提供参考。